research article suspended particulates concentration (pm...

Hindawi Publishing CorporationAdvances in MeteorologyVolume 2013, Article ID 457181, 10 pageshttp://dx.doi.org/10.1155/2013/457181

Research ArticleSuspended Particulates Concentration (PM10) underUnstable Atmospheric Conditions over Subtropical Urban Area(Qena, Egypt)

M. El-Nouby Adam1,2

1 Quality Assurance Unit (ALI), King Saud University, Riyadh 11491, Saudi Arabia2 Faculty of Science, Physics Department, South Valley University, Qena 83523, Egypt

Correspondence should be addressed to M. El-Nouby Adam; el nouby.adam [email protected]

Received 11 March 2013; Revised 8 June 2013; Accepted 12 June 2013

Academic Editor: Panuganti Devara

Copyright © 2013 M. El-Nouby Adam.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The main purpose of this study is to evaluate the suspended particulates (PM10) in the atmosphere under unstable atmospheric

conditions. The variation of PM10

was investigated and primary statistics were employed. The results show that, the PM10

concentrations values ranged from 6.00 to 646.74𝜇gm−3. The average value of PM10

is equal to 114.32𝜇gm−3. The high valueswere recorded in April and May (155.17𝜇gm−3 and 171.82 𝜇gm−3, respectively) and the low values were noted in February andDecember (73.86 𝜇gm−3and 74.05 𝜇gm−3, respectively). The average value of PM

10of the hot season (125.35 × 10−6 gm−3) was

higher than its value for the cold season (89.27 𝜇gm−3). In addition, the effect of weather elements (air temperature, humidity andwind) on the concentration of PM

10was determined. The multiple R between PM

10and these elements ranged from 0.05 to 0.47

and its value increased to reach 0.73 for the monthly average of the database used. Finally, the PM10concentrations were grouped

depending on their associated atmospheric stability class. These average values were equal to 122.80± 9𝜇gm−3 (highly unstable orconvective), 109.37± 12𝜇gm−3 (moderately unstable) and 104.42± 15 𝜇gm−3 (slightly unstable).

1. Introduction

Adam [1] reviewed that the diurnal variation of temperaturenear the ground is one of the key characteristics of theatmospheric boundary layer (ABL) over land.The convectiveatmosphere constitutes the daytime unstable ABL. It consistsof thermal plumes, that is, updrafts surrounded by largedowndrafts.They grow in the morning with the solar heatingof the surface of the earth. In the ABL, the air flow isturbulent because of two different mechanisms: friction withthe surface and surface heating by the sun. Adam and ElShazly [2] evaluated the atmospheric stability at Qena andstudied its diurnal variation which define the turbulent stateof the atmosphere and also reflect its dispersion capabilitiesthrough the period from 2001 to 2004. They found that thereare transitional hours inwhich the stability conditions changefrom the stable nighttime period to the unstable daytimehours (6:00 and 7:00 LST). During the daytime hours (8:00–15:00 LST), the atmosphere tends to be primarily unstable

with some neutral condition. In addition, no occurrencesof stable conditions were found in this period of time. Thisstudy is to assess the level of air pollution under unstableconditions after the transitional hours at midmorning hours(9:00–11:00 LST). This time is chosen because it is usuallya period of a high traffic and increase of the human activ-ities. Moreover, earlier studies of the diurnal cycle PM

10

concentrations observed elsewhere around the world showtwo peaks: one at midlate evening, 19:00–01:00 LST, andthe other one at midmorning, 8:00–11:00 LST (e.g., [3–9]).However, Corsmeier et al. [10] explained that at the midlateevening, peak in PM

10concentrations is associated with

wood burning emissions during evenings. During this perioddomestic emissions are at a maximum and, especially duringanticyclonic weather patterns, are emitted into very shallowboundary layers, resulting in the accumulation of pollutantsnear the surface. In addition, Trompetter et al. [11] mentionedthat the peaks observed during themorning period were traf-fic related sources alone and were unlikely to be a significant

2 Advances in Meteorology

360∘ (N)

30∘

60∘

90∘ (E)

120∘

150∘

180∘ (S)

210∘

240∘

(W) 270∘

300∘

330∘

Western desert Easterndesert

Qena

Mechanical scrapings and other human activities

High wayN

(a)

(b) (c)

∗

Figure 1: (a) Map of Egypt, location of studied area at Qena. (b)Wind rose for midmorning hours (9:00–11:00 LST) through the period from2002 to 2003. (c) ∗refers to South Valley University (SVU) meteorological research station.

source of pollution. It has therefore been hypothesized thatvertical mixing of elevated layers of pollution stored aloftdown to the surface may account for the increased morningconcentrations at the surface.

According to the World Health Organization (WHO)assessment of the burden of disease due to air pollution,more than 2 million premature deaths can be attributed tothe effects of urban outdoor air pollution and indoor airpollution every year. More than half of this disease burdenis founded by the populations of developing countries [12].Several published scientific studies indicate that there is anassociation between air pollutants to which people are rou-tinely exposed and a wide range of adverse health outcomes:Krupnick and Portney, [13]; Hall et al. [14]; Sommer et al.,[15]; Martuzzi et al., [16]; Hall et al., [17]; Scammell, [18]. Inaddition, Egyptian Environmental Affairs Agency (EEAA)reported that air pollution is one of the most importantchallenges and obstacles facing Egypt which have a majorimpact on increasing rate of development in all fields. How-ever, the problem had emerged with the significant increasein various manufacturing processes and its accompaniedemissions in air. Moreover, the terrible increase of vehiclesnumber operated with fossil fuel (as results of the increase ofpopulation number) which is considered the worst cause ofair pollution despite the fact of their necessity to modern lifeemits large quantities of gases [19].Themain legal instrument

dealing with environmental issues in Egypt is Law 4/1994which is commonly known as the law on protection of theenvironment. The law deals mostly with the protection ofthe environment against pollution. Law 4/1994 also stipulatesthe role of the EEAA as the main regulatory agency forenvironmental matters. Article 35 of the environmental law4/1994 and article 34 of its executive regulations define themaximum permissible levels of pollutants in ambient air.The national standard of the concentration of suspendedparticulatematters (PM

10), in terms ofmaximumpermissible

limits and exposure period, is 70 𝜇gm−3 (24-hrs) [20, 21].At Qena (26.2∘N, 32.7∘E, and 96m above mean sea level),

there are several reasons for suspended particulate mattersin the atmosphere. The main sources are sand and soilfrom the western and eastern hills which overlook the city,dusty roads with incomplete garbage removal, and the man-made processes such as agriculture, vehicles, and industry[22]. Qena is a place of many factories such as aluminumfactory, two factories of sugar, and a factory of cement [23].Furthermore, through the period of this study, there areemissions from mechanical scrapings such as constructionwork in new Qena city, reclamation of desert land foragriculture, and continuous drilling and building to completethe camp of South Valley University (see Figure 1). So, thiswork focuses on the study of suspended particulate matters(PM10) at Qena. Many sources offer good descriptions of

Advances in Meteorology 3

PM10concentrations in Egypt such as Sivertsen and El Seoud

[24], Elminir et al. [25], Elminir [26], and Zakey et al.[21]. However, Sivertsen and El Seoud [24] reported thatthe annual average concentrations of PM

10range between

100 𝜇gm−3 and 200𝜇gm−3 in urban and residential areasand between 200 𝜇gm−3 and 500𝜇gm−3 near industrialareas. In addition, Zakey et al. [21] have evaluated the PM

10

concentrations at 17 sites representing different activities(industrial, urban, and residential) in Greater Cairo area.They found that the PM

10concentrations were generally high

with yearly average values of 170 ± 25 𝜇gm−3. Moreover,at the region of this study, Adam [27] mentioned that theturbidity diurnal variationswere very limited during themosthours of the day except for sunrise and sunset hours. Hestudied the Angstrom turbidity coefficient (𝛽) through theperiod from 2001 to 2004, and themaximumhourly values of𝛽 were 0.208 (at 07.00 LST). This behavior may be due to thefrequent occurrence of inversions, allowing for the dispersionof aerosols [28, 29].

Themain aim is restricted to assess the level of particulatematters with an aerodynamic diameter ≤10 𝜇m (PM

10) at

midmorning hours (9:00–11:00 LST). In addition, the effectof weather elements on the concentration of suspendedparticulates (PM

10) was investigated. Moreover, the atmo-

spheric stability at the midmorning hours (9:00–11:00 LST)was determined, and the PM

10concentrations were grouped

depending on their associated atmospheric stability class.

2. Site and Data Set

The site of this study is located at South Valley Universityat Qena (26.20∘N, 32.70∘E, and 96.00m above mean sealevel). Figures 1(a) and 1(c) illustrate the map of Egypt andthe location of studied area at Qena (∗refers to SVU—meteorological research station). Qena is a city in the south-ern part of Egypt with 220,000 inhabitants (2009 estimate),capital of Qena governorate with 3.0 million inhabitants(2006 estimate) and an area of 1,800 km2, situated on theeast bank of the Nile between the western and eastern desert(Figure 1(a)). It lies within the subtropical region, and itsterrain is semidesert. The climate of Egypt is characterizedby small-scale depressions moving across the Great Sahara.This is due to khamsin depressions (March to May) andSudan monsoon trough (September to December). Theweather associated with these depressions is generally hot,dry, and dusty. From March to May, dry and strong south-westerly winds tend to occur over northern Africa when adesert depression develops and passes over strong barocliniczone extending from west to east parallel to the southernMediterranean coast [30]. Accordingly, the climate of Qenais characterized by a hot season from March to October anda cold season from November to February. In addition, aphenomenon of Qena climate is the hot spring wind thatblows across all Egypt. This wind usually arrives in April,but occasionally occurs in March and May [31]. Additionaldetail about particular cases of a Sahara cyclones and sub-synoptic phenomenon can be found in the work by Hassan[32].

The South Valley University (SVU) meteorologicalresearch station (Figure 1(c)) measured the particulatematters from 9:00 to 11:00 LST (2 hours). As a result, thisstudy deals with 288 data of PM

10samples which represent

the PM10

concentrations for midmorning hours (9:00–11:00LTS) during the period 2002-2003. Ten months (Jul. 02 toMay 03) PM

10samples were collected during the period of

this study. The number of samples represents more than 80%of the days for the all months in this study (except 57% forFeb. 02). The shortage of the samples in February and theunavailable data in March are due to technical reasons whichwere related to the sampler. Approximately, these monthsrepresent the different two seasons: hot season period (173samples) and cold season period (115 samples).

Graseby-Anderson (GMW) PM10

Sampler (Model 1200and serial no. 715) was used to collect atmospheric PM

10

in this meteorological research station. This sampler is usedto sample particulate matters with an aerodynamic diameterof 10.00 micron and less. The inlet head is symmetricaland therefore insensitive to wind direction and relativelyinsensitive to wind speed. The PM

10sampler draws air into

a specially shaped inlet at a flow rate of 40 ± 4 cubic feet perminute. PM

10particulate matters are collected on an 8 × 10

inch matted quartz fiber filter surface. The concentration ofPM10

particulate matters (in micron grams per cubic meter,𝜇gm−3) is calculated byweighing (Sartorius balance, TE 214S,max. 210 g, d = 0.1mg) the particulates collected on the filterand dividing by the measured air sample volume. Completeoperational details are contained in instruction and operationmanual High Volume PM

10Sampler [33]. The Egyptian

meteorology authority is responsible for the scientific adviceand calibration of the Egyptian Monitoring Network. ThePM10

sampler is calibrated according to a quality assuranceplan for air monitoring.

In the current study, the used data of atmospheric stabilityduring themidmorning hours (9:00–11:00 LST) was providedby Adam and El Shazly [2]. However, Pasquill-Gifford sta-bility classes were derived from the average values of globalsolar radiation (GSR), wind speed (Ws), and cloud amount(CA) during the interval from9:00 to 11:00 LST.Measurementof these parameters was carried out at Qena by SVU-meteorological research station. However, the atmosphericstability was classified, according to Pasquill-Gifford, as A(highly unstable or convective), B (moderately unstable), C(slightly unstable), D (neutral), E (moderately stable), andF (extremely stable). Later, stability G is also included torepresent low wind nighttime stable conditions [34].

3. Results and Discussion

3.1. Primary Statistics Analysis of PM10. In this study the

variation of PM10

concentrations was investigated to assessthe collected data. Figure 2(a) refers to the fluctuations ofPM10

values (𝜇gm−3) during the period of this study. Inaddition, primary statistics of these data were estimated.These statistics include the average (Ave.), maximum (Max.),minimum (Min.), coefficient of variance (CV), and a numberof available samples (𝑛) for each month, hot season, cold


Hot season Cold season Hot season700

350

01 271241211181151121916131

Number of observation

DC

(g m

−3)

(a)

10203040

1 271241211181151121916131Number of observation

T(∘

C)

(b)

755025

01 271241211181151121916131


Rh (%

)

(c)

10

5

01 271241211181151121916131


Ws (

m s−

1)

(d)

AA-B

BB-C

CC-D

D1 271241211181151121916131


Stab

ility

clas

ses

(e)

Figure 2: Variation of (a) PM10

concentration (gm−3), (b) airtemperature (𝑇, ∘C), (c) relative humidity (Rh, %), (d) wind speed(Ws, m s−1), and (e) atmospheric stability, for midmorning hours(9:00–11:00 LST) at Qena through the period 2002-03.

season, and all period of measurements. Table 1 summarizedthe descriptive statistics of PM

10. Both table and figures

reflect that, for the whole samples, PM10concentrations val-

ues ranged from6.00𝜇gm−3 to 646.74 𝜇gm−3 (themaximumwas in 19 April 2003, includes a dust and phenomenonof sand raised) and showed average of 114.32𝜇gm−3. Theaverage values of PM

10through the hot season period and

cold season period were 125.35 𝜇gm−3 and 89.27 𝜇gm−3,respectively.Themonthlymean of PM

10reflects a remarkable

variation from month to month. This behavior may be dueto the change of the atmospheric conditions such as windspeed, wind direction, humidity, and solar insulation levels(the effect of these atmospheric conditions on PM

10will be

studied in the next section). It can be seen that the high con-centrations were recorded in April and May (155.17 𝜇gm−3

and 171.82 𝜇gm−3, resp.). This behavior may be due to drysouthwesterly winds that tends to occur over northern Africawhen a desert depression develops and passes over strongbaroclinic zone extending from west to east parallel to thesouthern Mediterranean coast. The features which specifythe khamsin weather conditions (causes rising sand, sandstorm, and temperature) have attracted the attention of manymeteorologists in Egypt and was considered by their studies(e.g., [30, 32, 35, 36]). Wind direction data were recorded inthe mentioned interval (9:00 to 11:00 LST). Approximately,the wind direction is from the southwesterly direction inQena area during the time of the measurements (wind fromsector 225–285∘ occurred during 66% of the time of thesemeasurements, see Figure 1(b)). Both, the wind direction andwind speed are crucial parameters for these severe conditions.In addition, the low PM

10concentrations were noted in

February and December (73.86𝜇gm−2 and 74.05 𝜇gm−2,resp.). Low winter-time temperature (22∘C) often results instable weather conditions that aggravate the effects of particleemissions from the urban traffic [21]. The coefficient ofvariation (CV) which is defined as the ratio of the standarddeviation to the mean values gives an indication of thedispersion of the values [37]. CV values are equal to 72.30%.,68.34%, and 64.87% for all period, hot season, and coldseason, respectively. These values are relatively high and maybe due to the variability of meteorological conditions (see thenext section).Thehighest values of CVwere recorded inApril(87.90%). In April, it is expected due to the relatively highinstability of the local climate during this time interval, thatis, there is a high variation in climatic conditions from oneday to another [38].This variation in climatic conditions maybe due to the hot desert cyclones known as the khamsin. Theatmospheric stability (in April) was identified as A (11.90%),A to B (35.71%), B (16.67%), B to C (9.52%), C (16.67%), C toD (4.76%), and D (4.76%).

Moreover, the distribution of the samples at differentPM10

concentration classes was employed to illustrate thedifference between the levels of PM

10in both seasons. These

classes are ≤50, 51–70, 71–100, 101–200, 201–300, 301–400,401–500, 501–600, and 601–700 (𝜇gm−3). The percentageof samples for each class to the total samples (288) in thehot season period (𝑃

ℎ), cold season period (𝑃

𝑐), and all

measurement period (𝑃𝑡) was estimated and illustrated in

Table 2. From this table, one can conclude that the maximumvalues of 𝑃

𝑡occur at the class 101–200𝜇gm−3 (42.10%). At

PM10

classes ≤70𝜇gm−3, PM10

levels for cold season washigher than PM

10levels for hot season, while the opposite

happens at classes >70𝜇gm−3.The high values of PM10levels

in the hot season may be connected with the high insulationlevels and strong convective processes characteristic of aridregions. Accordingly, the fine dust particles are easily liftedto high altitudes and horizontally transported by synoptic-scale atmospheric disturbances to the areas thousands ofkilometers away from their source regions [39]. Adam and ELShazly [2] have studied the atmospheric stability during theperiod from 2001 to 2004 to explain the convective processescharacteristic of the area of this study.They found that duringthe daytime hours (8:00–15:00 LST), the atmosphere tends


Table 1: Average, maximum, and minimum of PM10 concentration (𝜇gm−3), coefficient of variation, and number of sampling during theperiod 2002-03.

Period Ave. Min. Max. CV 𝑛

Jan. 97.91 9.97 263.53 0.64 31Feb. 73.86 25.27 219.97 0.68 16Mar. No available dataApr. 155.17 27.70 646.74 0.86 43May 171.82 44.02 318.17 0.41 25Jun. No available dataJul. 99.38 10.53 175.83 0.47 28Aug. 103.17 14.30 201.02 0.40 37Sep. 116.34 8.22 259.76 0.45 34Oct. 107.47 29.55 283.41 0.47 33Nov. 95.57 6.49 317.74 0.68 28Dec. 74.05 16.35 118.95 0.42 13Cold season 89.27 6.49 317.74 0.70 88Hot season 125.35 8.22 646.74 0.71 200All samples 114.32 6.49 646.74 0.72 288

Table 2: Percentage of samples to the total samples for each PM10class for hot season (𝑃

ℎ), cold season (𝑃

𝑐), and all measurement

period (𝑃𝑡).

PM10 (𝜇gm−3) All period Hot season Cold season

≤50 18.6 12.7 28.051–70 8.9 7.5 11.271–100 18.6 17.9 19.6101–200 42.9 47.4 35.5201–300 8.6 11.6 3.7301–400 1.4 1.2 1.9401–500 0.4 0.6 0.0501–600 0.4 0.6 0.0601–700 0.4 0.6 0.0

to be primarily unstable with some neutral conditions. Theyconcluded that these results seem reliable if one considers thenature of the atmosphere in the study region with respect tothe behavior of global solar radiation, wind speed, and cloudsamount.They illustrated that the average hourly values (9:00–11:00 LST) of global solar radiation vary from 91.00mWcm−2through the hot season period to 60.00mWcm−2 throughcold season period with annual average equal to 81mWcm−2.In addition, thewinds are light atmost of the year.The averagehourly value (9:00–11:00 LST) of wind speed was 2.65m s−1(hot season period) and 1.96m s−1 (cold season period) withan annual average value equal to 2.31m s−1.

3.2.The Effect of Meteorological Factors in PM10 Level. Earlierstudies (e.g., [40–43]) reviewed that the meteorology playsan important role in ambient distributions of air pollution.The importance of meteorological factors in the transportand diffusion stage of air pollution cycle is well recognized.The entering of pollutants from the ground surface, their

residence in the atmosphere, and the formation of secondarypollutants is controlled not only by the rate of emission of thereactants into the air from the source, but also by wind speed,turbulence level, air temperature, and precipitation.Thus, it isoften important to understand the physical processes leadingto an observed concentration of pollutants at a given point.The variation of meteorological variables (𝑇, Rh, and Ws) isshown in Figures 2(b), 2(c), and 2(d) for the intervals of themeasurement samples (9:00–11:00 LST) through the periodunder study. Although [44] mentioned that the rainfall is oneof the reasons for low particulate pollutants as the pollutantsare washed out by rain, this factor is not important at Qena(it lies within the subtropical region characterized by hotand dry weather). Wet deposition by precipitation or wetremoval is one of the main mechanisms for removal ofaerosols from the atmosphere. In addition, this particulatepollutant changes the precipitation pattern and spins downthe hydrological cycle.

First, statistics of meteorological variables for the inter-vals of the measurement samples (9:00–11:00 LST) through2002-03 are listed in Table 3. This table includes average(Ave.), maximum (Max.), minimum (Min.), and coefficientof variance (CV). The number of observations (𝑛) for eachmonth, hot season, and cold season and within the wholeperiod of this study is reported in this table. Air temper-ature (𝑇) ranged between 14.20∘C (19/1/2003) and 37.40∘C(16/5/2003). The minimum of monthly average temperaturewas recorded in February 2003 (17.08∘C), and the maximumtemperature was recorded in May 2003 (33.27∘C). The sea-sonal average temperature varied from 22.46∘C (in cold sea-son) to 29.45∘C (in hot season).Wind speed (Ws) varied from1.00m s−1 to 7.50m s−1 with average value during the studyperiod equal to 2.74m s−1. The lowest of monthly averagewind speed was recorded in February 2003 (1.81m s−1) andhighest of monthly average wind speed was recorded in April2003 (3.08m s−1). Through the study period, the average


Table 3: Average, maximum, minimum of 𝑇, Rh, and Ws, coefficient of variation, and number of observation for each month, cold season,hot season, and all observation during the period (2002-03).

Period Weather elements Ave. Min. Max. CV 𝑛

Jan.𝑇 17.23 14.20 22.40 0.11

31Rh 47.81 27.00 67.00 0.23WS 2.50 0.50 6.50 0.42

Feb.𝑇 17.08 14.80 24.40 0.15

16Rh 44.06 22.00 60.00 0.20WS 1.81 1.00 6.50 0.81

Apr.𝑇 27.53 22.00 35.80 0.13

43Rh 23.37 10.00 42.00 0.31WS 3.08 0.50 8.00 0.58

May𝑇 33.27 24.20 37.40 0.09

25Rh 29.36 21.00 49.00 0.25WS 2.60 1.50 5.00 0.38

Jul.𝑇 29.02 22.70 36.10 0.15

28Rh 18.89 5.00 39.00 0.45WS 2.77 1.00 7.50 0.61

Aug.𝑇 28.77 21.80 36.10 0.16

37Rh 19.43 5.00 39.00 0.39WS 2.81 1.00 7.50 0.57

Sep.𝑇 30.08 21.80 36.40 0.13

34Rh 29.38 15.00 49.00 0.32WS 3.01 1.00 6.50 0.46

Oct.𝑇 28.34 23.60 32.80 0.10

33Rh 29.79 12.00 48.00 0.31WS 2.63 1.00 8.00 0.60

Nov.𝑇 27.95 21.80 36.10 0.15

28Rh 18.36 5.00 39.00 0.45WS 2.75 1.00 7.50 0.61

Dec.𝑇 29.73 23.60 36.10 0.15

13Rh 16.54 5.00 29.00 0.49WS 2.88 1.00 7.50 0.59

Cold season𝑇 22.46 36.10 14.20 0.30

88Rh 33.75 88.00 5.00 0.56WS 2.51 7.50 0.50 0.62

Hot season𝑇 29.25 37.40 21.80 0.14

200Rh 24.85 49.00 5.00 0.38WS 2.84 8.00 0.50 0.56

All samples𝑇 27.18 37.40 14.20 0.22

288Rh 27.38 67.00 5.00 0.48WS 2.74 8.00 0.50 0.57

value of wind speed was equal to 2.74m s−1. The seasonalaverage wind speed varied from 2.81m s−1 (in cold season) to2.86m s−1 (in hot season). The humidity ranged between 5%and 67% during the study period. The maximum humiditywas noted in January, and minimum humidity was recordedin July, August, November, and December. The seasonalaverages of humidity varied from 24.85% (in hot season) to33.75% (in cold season).

In addition, atmospheric stability was identified as A(14.04%), A to B (33.68%), B (25.61%), B to C (6.67%),

C (15.09%), C to D (3.16%), and D (1.75%). During thisinterval, the atmosphere tends to be primarily unstable (A–C) with some neutral condition (D). No occurrences of stableconditions (E–G) were found in this period of time. Theseresults seem reliable if one considers the atmosphere of thestudy region that is related to the behavior of GSR, Ws,and CA. As mentioned above, the suggestion of Pasquill andGifford for determining the nature of convection is basedon the variation of these parameters. These results reflectclearly that Qena is characterized to high extent with unstable


Table 4: Correlation coefficients (𝑅) between PM10 and weather elements for each month, cold season, hot season, and all observationsduring the period (2002-03). Multiple correlation and 𝐹-statistics values.

Period 𝑅 Multiple 𝑅 𝐹-statistics𝑇 Rh WS

Jan. −0.11 0.24 0.13 0.32 1.05Feb. 0.23 0.32 −0.21 0.44 0.95Apr. 0.37 0.12 −0.02 0.47 3.62∗

May −0.31 0.32 0.10 0.34 0.92Jul. −0.03 −0.18 −0.14 0.21 0.37Aug. −0.14 0.07 0.00 0.15 0.24Sep. −0.27 0.02 −0.16 0.33 1.19Oct. −0.03 0.32 −0.24 0.40 1.85Nov. −0.01 0.05 0.01 0.05 0.02Dec. −0.11 0.15 −0.26 0.32 0.34cold season −0.02 0.12 0.07 0.21 1.28hot season 0.09 0.14 −0.05 0.19 2.55∗

all data 0.16 0.04 0.00 0.33 8.94∗∗∗Significance level (significance 𝐹 at confidence level 99%).

atmosphere. This deduction is very important for the futureof the dispersion of the pollutants in this region, owing to thefact that unstable atmosphere strengthens the dispersion ofthe pollutants both vertically and horizontally [2]. Figure 2(e)shows the atmospheric stability classes for the midmorninghours through the period of this study.

Correlation analyses were carried out to quantify therelationship, if any, between the meteorological variables (𝑇,Rh, and Ws) and PM

10for each month, hot season, cold

season, and all the samples. These correlation coefficients(𝑅) have been done to assess the relationship between PM

10

and thesemeteorological variables. Table 4 summarized theseresults. For all cases, the results showed that there is nosignificant correlation between PM

10and these meteorolog-

ical parameters. The values of 𝑅 were weak (𝑅 < 0.5). Incontrast, a significant correlation was found between themonthly average of PM

10and temperature (𝑅 = 0.5). Figure 3

illustrated the variation of the monthly average of PM10

and the weather variables (𝑇, Rh, and Ws). This Figurereflects a weak correlation between PM

10and Ws (𝑅 = 0.3)

and a very weak correlation with Rh (𝑅 = −0.1). Similarresults were obtained by [5] in Northern Sweden. Theyfound low correlations (𝑅 < 0.5) between PM

10and the

weather parameters (𝑇 and Ws). They explained that thisis associated with the large variability of wind direction(Wd) that was very frequently observed in connection withlow Ws. In this study, PM

10concentrations at midmorning

with respect to wind directions for whole measuring periodare presented in Figure 4. The PM

10concentrations were at

higher values when prevailing wind direction was in thesector 225–285∘ (see Figure 1(b)).Wind from sector 225–285∘occurred during 66% of the time of these measurements.One of the possible explanations might be that the mostpolluted location, the main road to South Valley University,is opened from S-SW direction where the big crossroad witha highway. Another reason could be the influence of thegreat source of air pollution from mechanical scrapings and

other human activities to complete the camp of South ValleyUniversity.

Multiple correlation coefficients have been employed toassess the relationship between PM

10and these meteoro-

logical variables, and the results were presented in Table 4.From this table it was noted that particulate pollutants PM

10

showed a significant correlation with these parameters inApril (multiple 𝑅 ≈ 0.50). In addition, multiple 𝑅 is sta-tistically significance (𝐹-statistics = 3.62∗). As mentionedpreviously, in April, it is expected due to the relatively highinstability of the local climate during this time interval [38].This variation in climatic conditions may be due to thehot desert cyclones known as the khamsin. Highest PM

10

concentrations were also affected because of wind speed.Wind affects turbulence near the ground, thus affecting thedispersion of pollutants released into the air. Turbulence(largely the up and down motion of air) is generated in partby airflow over rough ground.The greater the wind speed, thegreater the turbulence and hence the greater the dispersion ofpollutants that are near the ground [45]The value of multiple𝑅 increased (𝑅 = 0.73) for the case of the monthly average ofPM10and the weather variables (𝑇, Rh, and Ws).

In order to see a clearer effect of the air temperature (𝑇) onPM10, further analysis was employed. The PM

10values were

classified according to the corresponding air temperature in1∘C intervals. Then, the averages of PM

10values per each

interval were estimated. Figure 5(a) shows the relationshipbetween 𝑇 (∘C) and the average values of PM

10for each

interval. The value of 𝑅 between them is equal to 0.54.Although, the correlation between PM

10and 𝑇 for each case

is a weak (see Table 4), there is a correlation between theaverage of PM

10for each interval and 𝑇 (𝑅 = 0.54). However,

the value of 𝑅 does not guarantee that the regression line canfit the data well [46]. A statistical analysis to determine if 𝑅is statistically significant was applied. The relation betweenthe residuals of the average of PM

10for each interval and 𝑇

as independent variable was implemented. The residual plot


15

20

25

30

35

60

90

120

150

180

DC

Janu

ary

Febr

uary

April

May July

Augu

st

Sept

embe

r

Oct

ober

Nov

embe

r

Dec

embe

r

Month

T(∘

C)

T (∘C)

DC

(g m

−3)

(a)

504030201060

90120150180

Rh (%

)

RhDC

Janu

ary

Febr

uary

April

May July

Augu

st

Sept

embe

r

Oct

ober

Nov

embe

r

Dec

embe

r

Month

DC

(g m

−3)

(b)

6090

120150180

DC

43210

Janu

ary

Febr

uary

April

May July

Augu

st

Sept

embe

r

Oct

ober

Nov

embe

r

Dec

embe

r

Month

Ws

Ws (

m s−

1)

DC

(g m

−3)

(c)

Figure 3:Monthly average variation of PM10concentration (gm−3),

and air temperature (a), relative humidity (b), and wind speed (c) atQena during the period from 2002 to 2003.

is shown in Figure 5(b). This figure shows a random patternfor the values of these residuals. Accordingly, the linearrelationship between the average of PM

10for each interval,

and 𝑇 provides a good fit to the data. In addition, the value of𝐹-statistics was equal to 2.00∗ (∗refers to significance of 𝐹 atconfidence level 99%).Therefore, the value of𝑅 is statisticallysignificant. This indicates that the values of average PM

10for

each intervals increase linearly with increased 𝑇. Krecl et al.[5] have classified PM

10according to the corresponding air

temperature in 5 ∘C intervals. A trend between the averagevalues of PM

10for each interval and the corresponding air

temperature (−30 to 10∘C) was evident. For the positive rangeof 𝑇, they found that the PM

10increased with increasing

𝑇. In addition, El Shazly [47] mentioned that the high dustcontent in the lower atmospheric layers arising from the

0

100

200

300

400

500

600

700

0 90 180 270 360Wd (deg)

PM10

(mg m

−3)

Figure 4: PM10 concentrations (gm−3) for difference wind direc-tion at Qena during the period from 2002 to 2003.

250

200

150

100

50

015 4035302520

T (∘C)

DC

(g m

−3)

(a)

75

0

−75

15 4035302520T (∘C)

Resid

uals

of D

C (g

m−3)

(b)

Figure 5: (a) Variation of the average PM10concentration for each

interval of T and (b) the residual plot.

well-developed vertical mixing of dust particles owing to thehigh temperature.

Finally, the PM10

concentrations were grouped depend-ing on their associated atmospheric stability class (A, A to B,B, B to C, and C). Then, the average concentration values ofPM10per each class were estimated.The values were 122.80±

9, 117.41 ± 7, 109.36 ± 12, 104.57 ± 8, and 104.42 ± 15(𝜇gm−2), respectively. Concerning the atmospheric stabilityclasses C toD andD, the average PM

10concentrations are not

considered statistically relevant. This is due to the fact that


both classes showed a low frequency of occurrence duringthe study period (4.76%). The variability of PM

10according

to the change of atmospheric stability classes is expected.Thismay be due to the variability of the dispersion coefficients inhorizontal (𝜎

𝑦) and vertical (𝜎

𝑧) directions. However, Adam

and El Shazly [2] estimated 𝜎𝑦and 𝜎

𝑧, by a method based on

a stability classification of the atmospheric conditions (after[48]), at Qena during the period from 2001 to 2004. Theyfound that the values of 𝜎

𝑦and 𝜎

𝑧decreased with increasing

instability conditions.

4. Conclusions

The present study is to assess the levels of PM10at subtropical

urban area.The database used values of PM10 under unstableatmospheric conditions. By making use of the availableinformation, the following conclusions are drawn to the bestof our knowledge about the PM

10and its relationship with

the weather elements at the study region.The results could besummarized in the following outcomes.

(i) For the midmorning hours (9:00–11:00 LST), PM10

concentrations showed average of 114.32𝜇gm−3. Thisaverage was equal to 89.27𝜇gm−3 at the cold seasonand 125.35 𝜇gm−3 at the hot season. The high valuesof PM

10levels in the hot season may be connected

with the high insulation levels and strong convectiveprocesses characteristic of arid regions.

(ii) During the period of dust storm (the khamsinweather conditions), the PM

10levels at Qena increase

to reach a relatively high values (646.74𝜇gm−3 at 19April 03).

(iii) PM10was shown a poor correlation with both Rh (𝑅

ranged from −0.12 to 0.32) and Ws (𝑅 ranged from−0.26 to 0.16). Although a weak correlation betweenPM10

and 𝑇 was found (𝑅 ranged from −0.27 to0.37), the values of 𝑅 between the averages of PM

10

for each interval values of temperature (1∘C) and𝑇 was equal to 0.54. In additions, for the monthlyaverage values there is a significant correlation thatwas found between the PM

10and temperature (𝑅 =

0.5). The monthly average of the PM10

and theweather elements showed a significant correlation.The multiple 𝑅 was equal to 0.73.

(iv) Under unstable atmospheric conditions, the PM10

concentrations decrease with decreasing instabilityof the atmosphere. The average values of PM

10were

equal to 122.80±9 𝜇gm−3 (highly unstable or convec-tive), 109.37 ± 12 𝜇gm−3 (moderately unstable), and104.42 ± 15 𝜇gm−3 (slightly unstable).

Acknowledgments

The editor of the journal, Dr. P. Devara, and the anony-mous reviewers for their constructive comments and sug-gestions are highly acknowledged. The author would liketo thank the Program Research Center at Arabic Language

Institute, Deanship of Scientific Research, King Saud Uni-versity, Riyadh, Saudi Arabia for funding and supportingthis research.

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